CN110673148A - Active sonar target real-time track resolving method - Google Patents

Active sonar target real-time track resolving method Download PDF

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CN110673148A
CN110673148A CN201911021507.4A CN201911021507A CN110673148A CN 110673148 A CN110673148 A CN 110673148A CN 201911021507 A CN201911021507 A CN 201911021507A CN 110673148 A CN110673148 A CN 110673148A
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高貂林
徐娜
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Haiying Enterprise Group Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/02—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems using reflection of acoustic waves
    • G01S15/50—Systems of measurement, based on relative movement of the target
    • G01S15/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G01S15/588—Velocity or trajectory determination systems; Sense-of-movement determination systems measuring the velocity vector
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/02—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems using reflection of acoustic waves
    • G01S15/50—Systems of measurement, based on relative movement of the target
    • G01S15/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G01S15/62—Sense-of-movement determination

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Abstract

The invention provides an active sonar target real-time track resolving method, and belongs to the technical field of underwater acoustic detection. Predicting the position information of the target in each second through a Kalman filter; judging and eliminating outliers; and resolving the navigation speed and the course information of the target by using the position information of the predicted target. Compared with the traditional unscented Kalman filtering method and the existing anti-outlier Kalman filtering method, the method improves the filtering precision and the target information refresh rate, and achieves the aims of improving the system target resolving efficiency and shortening the system reaction time.

Description

Active sonar target real-time track resolving method
Technical Field
The invention relates to the technical field of underwater acoustic detection, in particular to an active sonar target real-time track resolving method.
Background
Course speed is an important characteristic of the offshore target, and the fact that course speed information of the target can be accurately obtained has very important significance for tracking and identifying the offshore target.
The target course speed resolving method has been widely researched, and particularly, the method for resolving the target course speed by using the tracking information received in the transmitting period is more researched, and the resolving research for the target course speed per second is less. Because the direction and the distance of the target are needed in the process of calculating the flight path, the more accurate the direction and the distance of the target detected by the sonar are, the smaller the error of calculating the flight path is.
The active sonar is a sonar which autonomously emits acoustic signals and accurately acquires the direction and the distance of a target by detecting a target emission echo, while the passive sonar usually only acquires the direction of the target and cannot acquire the distance of the target. Because the sound velocity in water is low, the transmitting period of the active sonar usually takes several seconds or even dozens of seconds, and the processing of the active sonar such as searching and tracking the target is carried out according to the transmitting period, so that the target information refreshing rate is low. In an actual situation, the calculation of the target course speed is a complex process, the motion tracks of the ship and the target are influenced by a plurality of external factors such as sea conditions, water flows and the like besides the inherent detection error of the sonar, and the factors can cause the ship and the target to deviate from the preset motion tracks at a certain moment, so that the filter can disperse, and the track calculation error is increased.
Disclosure of Invention
The invention aims to provide a method for solving the real-time track of an active sonar target, which is used for solving the problems of complexity, instantaneity, low precision and the like of the existing target course speed solution.
In order to solve the technical problem, the invention provides an active sonar target real-time track resolving method, which comprises the following steps:
predicting the position information of the target in each second through a Kalman filter;
judging and eliminating outliers;
and resolving the navigation speed and the course information of the target by using the position information of the predicted target.
Optionally, the predicting the position information of the target per second through the kalman filter includes:
firstly, initializing parameters:
Figure BDA0002247371810000021
wherein,
Figure BDA0002247371810000022
is an initial value of a state measurement value, x0Is the initial value of the measured value, E is the average,
Figure BDA0002247371810000023
is the state error covariance matrix, T is the transpose;
second, calculate Sigma point:
Figure BDA0002247371810000024
wherein,
Figure BDA0002247371810000025
is the predicted value, χ, of the last moment of the state measurementk-1Is a Sigma point, Pk-1Is the state error covariance matrix at the previous moment, k is the discrete sampling point, m is the number of state parameters, λ is the scale factor, λ ═ α2(m + kappa) -m, alpha is Sigma Point chik-1To the predicted value
Figure BDA0002247371810000026
Distance of (10)-4Alpha is not less than 1, kappa is constant and is 0 or 3-m;
thirdly, calculating a weight coefficient:
Figure BDA0002247371810000027
Wi m＝Wi cλ/(m + λ), where Wi m、Wi cAre the first and second order weight coefficients of the ith of the Sigma point,
Figure BDA0002247371810000028
is the initial value of the first and second order weight coefficient of the Sigma point, taking kappa as a constant and taking 0 or 3-m; β ═ 2;
step four, time updating: obtaining x by a nonlinear state function f (.)k|k-1＝f(χk-1) Therein, xk|k-1Is the Sigma point under the status vector;
computing a predictor of a state vector
Figure BDA0002247371810000029
Wherein, L is the length of a Sigma point, L is 2m, and m is the number of state parameters;
computing
Figure BDA00022473718100000210
Wherein, Pk|k-1A predicted value of state error covariance;
fifthly, measurement updating: according to the calculated chik|k-1 and a nonlinear measurement function h (. -) to obtain yk|k-1＝h(χk|k-1) Wherein, yk|k-1Is the predicted value of the measurement vector;
computing
Figure BDA00022473718100000211
Sigma points of the measurement vector;
computing
Figure BDA00022473718100000212
Is an autocovariance matrix;
computing
Figure BDA00022473718100000213
Is a cross-covariance matrix;
computing
Figure BDA00022473718100000214
KkIs a filter gain matrix;
computing
Figure BDA00022473718100000215
Is the state vector estimate at the current time, ykIs the actual measurement vector;
computing
Figure BDA00022473718100000216
PkIs an estimate of the error covariance at the current time, Pk|k-1A prediction of state error covariance.
Optionally, the determining outliers and rejecting includes:
and (4) field value judgment: order to
Figure BDA0002247371810000031
ekIs the residual error, yk|k-1Is the predicted value of the measurement vector,
Figure BDA0002247371810000032
is the Sigma point of the measurement vector, when the filter is stable, the standard deviation Sigma of the residual error ism is the number of state parameters, Wi cIs the second order weight coefficient of the ith of the Sigma point, T is the transposed symbol;
identifying the observed value ykWhether each component of (a) is a outlier: the discriminant is | (e)k)i|≤Cσi,iWhere σ isi,iAs the i-th element on the innovation standard deviation diagonal, (e)k)iIs ekC is 3 or 4; (y)k)iRepresents the ith observation;
if the above equation holds, then (y)k)iIs a normal observed quantity, otherwise (y)k)iThe wild value is obtained;
wild value elimination: when (y)k)iWhen the distortion or outlier is present, K is adjustedkTo obtain an accurate estimate; calculating gain matrix K by filteringkThen, adjusting the size of the filter according to the convergence of the filter, and enabling Kk＝mKk(0 < m < 1), and then continuing to evaluate the filtered estimate
Figure BDA0002247371810000034
Sum filter error covariance PkTherefore, the influence of the outlier point is eliminated while the target state parameter is estimated.
Optionally, the calculating the speed and heading information of the target by using the position information of the predicted target includes:
solving the target course navigational speed: the sonar carrier is provided with a GPS and an inertial navigation system, and the geographic coordinate and the ground course navigation speed of the sonar carrier can be obtained in real time; position information M of each second target relative sonar carrier output by combining Kalman filterk(Rk,αk)，RkIs the target distance, αkAnd deducing the geographical coordinates of the target every second, and estimating the absolute course and the navigational speed of the target through continuous observation of the target.
The invention provides an active sonar target real-time track resolving method, which predicts the position information of a target in each second through a Kalman filter; judging and eliminating outliers; and resolving the navigation speed and the course information of the target by using the position information of the predicted target. Compared with the traditional unscented Kalman filtering method and the existing anti-outlier Kalman filtering method, the method improves the filtering precision and the target information refresh rate, and achieves the aims of improving the system target resolving efficiency and shortening the system reaction time.
Drawings
FIG. 1 is a schematic flow chart of the active sonar target real-time track solution method provided by the invention;
FIG. 2 is a schematic diagram of actual and predicted motion trajectories of a target;
FIG. 3 is a schematic view of course resolving errors at different course speeds;
FIG. 4 is a schematic diagram of the speed resolving error at different heading speeds.
Detailed Description
The following describes the active sonar target real-time track solution method provided by the present invention in further detail with reference to the accompanying drawings and specific embodiments. Advantages and features of the present invention will become apparent from the following description and from the claims. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.
Example one
The invention provides an active sonar target real-time track resolving method, and the flow of the active sonar target real-time track resolving method is shown in a figure 1. The method comprises the steps of predicting the position information of a target in each second through a Kalman filter; judging and eliminating outliers; and resolving the navigation speed and the course information of the target by using the position information of the predicted target.
The conditions of the embodiment are that the target is set to do uniform linear motion, the initial azimuth is 45 degrees, the initial distance is 6000m, white noise with the average value of 0 and the standard deviation of 160m is added into the distance measurement value, white noise with the average value of 0 and the standard deviation of 1.7 degrees is added into the azimuth measurement value to simulate the actual detection error of the sonar. Setting the navigational speeds to be 4kn, 6kn, 8kn, 10kn and 12kn respectively, setting the headings to be 45 degrees, 90 degrees, 135 degrees, 180 degrees and 225 degrees respectively, and adding three field values in each track. The specific implementation steps are as follows:
initializing parameters:
Figure BDA0002247371810000041
wherein x is0Is the initial value of the measured value and,is the initial value of the state measurement, E is the statistical mean,
Figure BDA0002247371810000043
is the state error covariance matrix and T is the transpose. Calculate Sigma point:wherein,is the predicted value, χ, of the last moment of the state measurementk-1Is a Sigma point, Pk-1Is the state error covariance matrix at the previous moment, k is the discrete sampling point, m is the number of state parameters, λ is the scale factor, λ ═ α2(m + kappa) -m, alpha is Sigma Point chik-1To the predicted valueDistance of (10)-4Alpha is not less than 1, kappa is constant and is 0 or 3-m. Calculating a weight coefficient:
Figure BDA0002247371810000047
Wi m＝Wi cλ/(m + λ), where Wi m、Wi cIs the first and second order weight of the ith of the Sigma pointThe coefficients of which are such that,the first and second order weight coefficients of a Sigma point are initial values, k is a constant and is generally 0 or 3-m, β is 2, m is the number of state parameters, and λ is a scale factor.
And (3) time updating: obtaining x by a nonlinear state function f (.)k|k-1＝f(χk-1) Therein, xk|k-1Is the Sigma point under the status vector.
According to the above-mentioned X calculatedk|k-1Calculating
Figure BDA0002247371810000049
Wherein,
Figure BDA00022473718100000410
the predicted value of the state vector, L is the length of the Sigma point, L is 2m, m is the number of state parameters, Wi mIs the first order weight coefficient of the ith of the Sigma point.
According to the above-mentioned X calculatedk|k-1、
Figure BDA0002247371810000051
ComputingWherein, Pk|k-1Prediction of state error covariance, Wi cIs the second order weight coefficient of the ith of the Sigma point. And (3) measurement updating: according to the calculated chik|k-1And a nonlinear measurement function h (.) to obtain yk|k-1＝h(χk|k-1) Wherein, yk|k-1Is the predicted value of the measurement vector.
Computing
Figure BDA0002247371810000053
Is the Sigma point of the measurement vector, Wi mIs the first order weight coefficient of the ith of the Sigma point.
ComputingIs an autocovariance matrix, Wi cIs the second order weight coefficient of the ith of the Sigma point.
Computing
Figure BDA0002247371810000055
Is a cross covariance matrix.
Computing
Figure BDA0002247371810000056
KkIs the filter gain matrix.
Computing
Figure BDA0002247371810000057
Is the state vector estimate at the current time, ykIs the actual measurement vector.
Computing
Figure BDA0002247371810000058
PkIs an estimate of the error covariance at the current time, Pk|k-1A prediction of state error covariance.
And (4) field value judgment: order to
Figure BDA0002247371810000059
ekIs the residual error, yk|k-1Is the predicted value of the measurement vector,is the Sigma point of the measurement vector, when the filter is stable, the standard deviation Sigma of the residual error is
Figure BDA00022473718100000511
m is the number of state parameters, Wi cIs the ith second order weight coefficient of the Sigma point, T is the transposed symbol;
can give an observed value ykThe definition and identification method for determining whether each component is a outlier, the discriminant is | (e)k)i|≤Cσi,iWhere σ isi,iAs the i-th element on the innovation standard deviation diagonal, (e)k)iIs ekC may take 3 or 4. (y)k)iRepresenting the ith observation. If the above equation holds, it can be considered that (y)k)iIs a normal observation, otherwise (y) is consideredk)iThe wild value is obtained.
Wild value elimination: when (y)k)iWhen the distortion or outlier is present, K is adjustedkTo obtain an accurate estimate; calculating gain matrix K by filteringkThen, the filter convergence can be adjusted to make Kk＝mKk(0 < m < 1), and then continuing to evaluate the filtered estimate
Figure BDA00022473718100000512
Sum filter error covariance PkTherefore, the influence of the outlier point is eliminated while the target state parameter is estimated.
Solving the target course navigational speed: the sonar carrier is provided with a GPS and an inertial navigation system, and the geographic coordinate and the ground course navigation speed of the sonar carrier can be obtained in real time; position information M of each second target relative sonar carrier output by combining Kalman filterk(Rk,αk)，RkIs the target distance, αkAnd deducing the geographical coordinates of the target every second, and estimating the absolute course and the navigational speed of the target through continuous observation of the target.
The performance of the real-time track resolving method of the active sonar target is judged and mainly comprises the following three parts: the difference between the target actual motion trajectory and the predicted trajectory is the first, and the performance of the difference is shown in fig. 2; secondly, the target course resolving error, and the performance of the method is shown in figure 3; thirdly, the target speed resolving error, and the performance of the method is shown in figure 4.
The above description is only for the purpose of describing the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention, and any variations and modifications made by those skilled in the art based on the above disclosure are within the scope of the appended claims.

Claims (4)

1. An active sonar target real-time track resolving method is characterized by comprising the following steps:
predicting the position information of the target in each second through a Kalman filter;
judging and eliminating outliers;
and resolving the navigation speed and the course information of the target by using the position information of the predicted target.
2. The active sonar target real-time trajectory resolution method of claim 1, wherein predicting position information for the target per second via a kalman filter comprises:
firstly, initializing parameters:
Figure FDA0002247371800000011
wherein,
Figure FDA0002247371800000012
is an initial value of a state measurement value, x0Is the initial value of the measured value, E is the average,
Figure FDA0002247371800000013
is the state error covariance matrix, T is the transpose;
second, calculate Sigma point:
Figure FDA0002247371800000014
wherein,
Figure FDA0002247371800000015
is the predicted value, χ, of the last moment of the state measurementk-1Is a Sigma point, Pk-1Is the state error covariance matrix at the previous moment, k is the discrete sampling point, m is the number of state parameters, λ is the scale factor, λ ═ α2(m + kappa) -m, alpha is Sigma Point chik-1To the predicted value
Figure FDA0002247371800000016
Distance of (10)-4Alpha is not less than 1, kappa is constant and is 0 or 3-m;
thirdly, calculating a weight coefficient:
Figure FDA0002247371800000017
Wi m＝Wi cλ/(m + λ), where Wi m、Wi cAre the first and second order weight coefficients of the ith of the Sigma point,
Figure FDA0002247371800000018
is the initial value of the first and second order weight coefficient of the Sigma point, taking kappa as a constant and taking 0 or 3-m; β ═ 2;
step four, time updating: obtaining x by a nonlinear state function f (.)k|k-1＝f(χk-1) Therein, xk|k-1Is the Sigma point under the status vector;
computing a predictor of a state vector
Figure FDA0002247371800000019
Wherein, L is the length of a Sigma point, L is 2m, and m is the number of state parameters;
computing
Figure FDA00022473718000000110
Wherein, Pk|k-1A predicted value of state error covariance;
fifthly, measurement updating: according to the calculated chik|k-1And a nonlinear measurement function h (.) to obtain yk|k-1＝h(χk|k-1) Wherein, yk|k-1Is the predicted value of the measurement vector;
computing Sigma points of the measurement vector;
computing
Figure FDA00022473718000000113
PykykIs an autocovariance matrix;
computing
Figure FDA0002247371800000021
Figure FDA0002247371800000022
Is a cross-covariance matrix;
computing
Figure FDA0002247371800000023
KkIs a filter gain matrix;
computing
Figure FDA0002247371800000025
Is the state vector estimate at the current time, ykIs the actual measurement vector;
computing
Figure FDA0002247371800000026
PkIs an estimate of the error covariance at the current time, Pk|k-1A prediction of state error covariance.
3. The active sonar target real-time track resolution method of claim 2, wherein determining outliers and rejecting comprises:
and (4) field value judgment: order to
Figure FDA0002247371800000027
ekIs the residual error, yk|k-1Is the predicted value of the measurement vector,
Figure FDA0002247371800000028
is the Sigma point of the measurement vector, when the filter is stable, the standard deviation Sigma of the residual error is
Figure FDA0002247371800000029
m is the number of state parameters, Wi cIs the second order weight coefficient of the ith of the Sigma point, T is the transposed symbol;
identifying the observed value ykWhether each component of (a) is a outlier: the discriminant is | (e)k)i|≤Cσi,iWhere σ isi,iAs the i-th element on the innovation standard deviation diagonal, (e)k)iIs ekC is 3 or 4; (y)k)iRepresents the ith observation;
if the above equation holds, then (y)k)iIs a normal observed quantity, otherwise (y)k)iThe wild value is obtained;
wild value elimination: when (y)k)iWhen the distortion or outlier is present, K is adjustedkTo obtain an accurate estimate; calculating gain matrix K by filteringkThen, adjusting the size of the filter according to the convergence of the filter, and enabling Kk＝mKk(0 < m < 1), and then continuing to evaluate the filtered estimate
Figure FDA00022473718000000210
Sum filter error covariance PkTherefore, the influence of the outlier point is eliminated while the target state parameter is estimated.
4. The active sonar target real-time path solution method of claim 3, wherein solving for the speed and heading information of the target using the predicted target location information comprises:
solving the target course navigational speed: the sonar carrier is provided with a GPS and an inertial navigation system, and the geographic coordinate and the ground course navigation speed of the sonar carrier can be obtained in real time; position information M of each second target relative sonar carrier output by combining Kalman filterk(Rk,αk)，RkIs the distance of the target, αkThe direction of the target is deduced, the geographical coordinates of the target every second are deduced, and the absolute course and the navigational speed of the target are estimated through continuous observation of the target.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111504305A (en) * 2020-03-12 2020-08-07 国家深海基地管理中心 Underwater acoustic communication-MEMS sensor collaborative navigation positioning method and system
CN111999735A (en) * 2020-09-11 2020-11-27 杭州瑞利海洋装备有限公司 Dynamic and static target separation method based on radial velocity and target tracking
CN112051573A (en) * 2020-08-27 2020-12-08 北京航管科技有限公司 Flight path processing method and device and storage medium
CN112083466A (en) * 2020-09-14 2020-12-15 中国人民解放军61540部队 Submarine transponder positioning method and system considering time deviation
CN113671479A (en) * 2021-05-24 2021-11-19 四川九洲防控科技有限责任公司 Method, device and computer readable storage medium for determining track initiation
CN115877328A (en) * 2023-03-06 2023-03-31 成都鹰谷米特科技有限公司 Signal receiving and transmitting method of array radar and array radar

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103033186A (en) * 2012-12-30 2013-04-10 东南大学 High-precision integrated navigation positioning method for underwater glider
CN103743395A (en) * 2014-01-17 2014-04-23 哈尔滨工程大学 Time delay compensation method in inertia gravity matching combined navigation system
CN105783909A (en) * 2014-12-26 2016-07-20 中国科学院沈阳自动化研究所 HOV mixed underwater navigation locating method based on UKF and PSO-SVM
CN108519615A (en) * 2018-04-19 2018-09-11 河南科技学院 Mobile robot autonomous navigation method based on integrated navigation and Feature Points Matching
CN108645415A (en) * 2018-08-03 2018-10-12 上海海事大学 A kind of ship track prediction technique
CN109143224A (en) * 2018-08-28 2019-01-04 中国电子科技集团公司第三十六研究所 A kind of multiple target correlating method and device
CN109447133A (en) * 2018-10-11 2019-03-08 东南大学 A kind of elimination method of the location information outlier based on SVR algorithm
CN110286357A (en) * 2019-07-10 2019-09-27 中国电子科技集团公司第三十六研究所 A kind of target motion positions method based on Underwater Detection

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103033186A (en) * 2012-12-30 2013-04-10 东南大学 High-precision integrated navigation positioning method for underwater glider
CN103743395A (en) * 2014-01-17 2014-04-23 哈尔滨工程大学 Time delay compensation method in inertia gravity matching combined navigation system
CN105783909A (en) * 2014-12-26 2016-07-20 中国科学院沈阳自动化研究所 HOV mixed underwater navigation locating method based on UKF and PSO-SVM
CN108519615A (en) * 2018-04-19 2018-09-11 河南科技学院 Mobile robot autonomous navigation method based on integrated navigation and Feature Points Matching
CN108645415A (en) * 2018-08-03 2018-10-12 上海海事大学 A kind of ship track prediction technique
CN109143224A (en) * 2018-08-28 2019-01-04 中国电子科技集团公司第三十六研究所 A kind of multiple target correlating method and device
CN109447133A (en) * 2018-10-11 2019-03-08 东南大学 A kind of elimination method of the location information outlier based on SVR algorithm
CN110286357A (en) * 2019-07-10 2019-09-27 中国电子科技集团公司第三十六研究所 A kind of target motion positions method based on Underwater Detection

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
杨宜禾 等: "《成像跟踪技术导论》", 30 April 1992 *
赵俊廷: "《化工原理实验》", 30 September 2011 *
韩珺礼 等, 国防工业出版社 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111504305A (en) * 2020-03-12 2020-08-07 国家深海基地管理中心 Underwater acoustic communication-MEMS sensor collaborative navigation positioning method and system
CN111504305B (en) * 2020-03-12 2021-05-28 国家深海基地管理中心 Underwater acoustic communication-MEMS sensor collaborative navigation positioning method and system
CN112051573A (en) * 2020-08-27 2020-12-08 北京航管科技有限公司 Flight path processing method and device and storage medium
CN112051573B (en) * 2020-08-27 2024-06-11 民航空管技术装备发展有限公司 Track processing method and device and storage medium
CN111999735A (en) * 2020-09-11 2020-11-27 杭州瑞利海洋装备有限公司 Dynamic and static target separation method based on radial velocity and target tracking
CN111999735B (en) * 2020-09-11 2023-10-03 杭州瑞利海洋装备有限公司 Dynamic and static target separation method based on radial speed and target tracking
CN112083466A (en) * 2020-09-14 2020-12-15 中国人民解放军61540部队 Submarine transponder positioning method and system considering time deviation
CN112083466B (en) * 2020-09-14 2024-01-26 中国人民解放军61540部队 Submarine transponder positioning method and system taking time deviation into consideration
CN113671479A (en) * 2021-05-24 2021-11-19 四川九洲防控科技有限责任公司 Method, device and computer readable storage medium for determining track initiation
CN115877328A (en) * 2023-03-06 2023-03-31 成都鹰谷米特科技有限公司 Signal receiving and transmitting method of array radar and array radar

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Application publication date: 20200110